Authors:
Richard May
1
;
Tobias Niemand
2
;
Paul Scholz
3
and
Thomas Leich
1
Affiliations:
1
Harz University of Applied Sciences, Wernigerode, Germany
;
2
Siemens Mobility GmbH, Brunswick, Germany
;
3
Hilti AG, Thüringen, Austria
Keyword(s):
Monitoring, Prediction, Machine Learning, Systematic Literature Review, Cluster Analysis.
Abstract:
Although machine learning methods for industrial maintenance systems have already been well described in recent years, their practical implementation is only slowly taking place. One of the reasons is a lack of comparable analyses of machine learning systems. To address this gap, we first conducted a systematic literature review (2012–2021) of 104 monitoring and prediction systems. Second, we extracted 5 design patterns (i.e., high-level construction manuals) based on a k-means cluster analysis. Our results show that monitoring and prediction systems mainly differ in their choice of operations. However, they usually share similar learning strategies (i.e., supervised learning) and tasks (i.e., classification, regression). With our work, we aim to help researchers and practitioners to understand common characteristics, contexts, and trends.